For a 0.2.1 release

Project.toml

@@ -1,15 +1,15 @@
 name = "LearnTestAPI"
 uuid = "3111ed91-c4f2-40e7-bb19-7f6c618409b8"
 authors = ["Anthony D. Blaom <[email protected]>"]
-version = "0.2.0"
+version = "0.2.1"
 
 [deps]
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 InteractiveUtils = "b77e0a4c-d291-57a0-90e8-8db25a27a240"
 IsURL = "ceb4388c-583f-448d-bb30-00b11e8c5682"
 LearnAPI = "92ad9a40-7767-427a-9ee6-6e577f1266cb"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
-MLUtils = "f1d291b0-491e-4a28-83b9-f70985020b54"
+MLCore = "c2834f40-e789-41da-a90e-33b280584a8c"
 MacroTools = "1914dd2f-81c6-5fcd-8719-6d5c9610ff09"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 ScientificTypes = "321657f4-b219-11e9-178b-2701a2544e81"
@@ -27,7 +27,7 @@ InteractiveUtils = "<0.0.1, 1"
 IsURL = "0.2.0"
 LearnAPI = "0.2.0"
 LinearAlgebra = "<0.0.1, 1"
-MLUtils = "0.4"
+MLCore = "1.0.0"
 MacroTools = "0.5"
 Random = "<0.0.1, 1"
 ScientificTypes = "3.0.2"

src/LearnTestAPI.jl

@@ -30,7 +30,7 @@ module LearnTestAPI
 using LearnAPI
 import Test
 import Serialization
-import MLUtils
+import MLCore
 import StableRNGs
 import InteractiveUtils
 import MacroTools

src/learners/ensembling.jl

@@ -76,7 +76,7 @@ function LearnAPI.fit(learner::Ensemble, data; verbosity=1)
     rng = deepcopy(learner.rng) # to prevent mutation of `learner`!
     n = learner.n
 
-    # ensure data can be subsampled using MLUtils.jl, and that we're feeding the atomic
+    # ensure data can be subsampled using MLCore.jl, and that we're feeding the atomic
     # `fit` data in an efficient (pre-processed) form:
 
     observations = obs(atom, data)
@@ -85,12 +85,12 @@ function LearnAPI.fit(learner::Ensemble, data; verbosity=1)
     models = []
 
     # get number of observations:
-    N = MLUtils.numobs(observations)
+    N = MLCore.numobs(observations)
 
     # train the ensemble:
     for _ in 1:n
         bag = rand(rng, 1:N, N)
-        data_subset = MLUtils.getobs(observations, bag)
+        data_subset = MLCore.getobs(observations, bag)
         # step down one verbosity level in atomic fit:
         model = fit(atom, data_subset; verbosity=verbosity - 1)
         push!(models, model)
@@ -125,7 +125,7 @@ function LearnAPI.update(
 
     atom = learner.atom
     observations = obs(atom, data)
-    N = MLUtils.numobs(observations)
+    N = MLCore.numobs(observations)
 
     # initialize:
     models = model.models
@@ -134,7 +134,7 @@ function LearnAPI.update(
     # add new regressors to the ensemble:
     for _ in 1:Δn
         bag = rand(rng, 1:N, N)
-        data_subset = MLUtils.getobs(observations, bag)
+        data_subset = MLCore.getobs(observations, bag)
         model = fit(atom, data_subset; verbosity=verbosity-1)
         push!(models, model)
     end

src/logging.jl

@@ -211,7 +211,7 @@ const SELECTED_FOR_FIT = """
     Testing that we can select all observations from `_data = LearnAPI.obs(learner,
     data)`, using the interface declared by `LearnAPI.data_interface(learner)`. For
     example, if this interface is `LearnAPI.RandomAccess()`, the attempted selection is
-    `data3 = MLUtils.getobs(_data, 1:MLUtils.numobs(_data))`. For this interface, also
+    `data3 = MLCore.getobs(_data, 1:MLCore.numobs(_data))`. For this interface, also
     testing that we can call `fit` on the selection, obtaining a new `fit` result
     `model3`.
 
@@ -221,7 +221,7 @@ const SELECTED = """
     Testing that we can select all observations from `_X = LearnAPI.obs(model, X)`,
     using the interface declared by `LearnAPI.data_interface(learner)`. For example, if
     this interface is `LearnAPI.RandomAccess()`, the attempted selection is
-    `X3 = MLUtils.getobs(_X, 1:MLUtils.numobs(_X))`.
+    `X3 = MLCore.getobs(_X, 1:MLCore.numobs(_X))`.
 
   """
 const PREDICT_ON_SELECTIONS1 = """

src/testapi.jl

@@ -48,7 +48,7 @@ The following are *not* tested:
 - The veracity of `LearnAPI.is_pure_julia(learner)`.
 
 - The second of the two contracts appearing in the
-  [`LearnAPI.target_observation_scitype`](@extref) docstring. The first contract is only
+  [`LearnAPI.target_observation_scitype`](@ref) docstring. The first contract is only
   tested if `LearnAPI.data_interface(learner)` is `LearnAPI.RandomAccess()` or
   `LearnAPI.FiniteIterable()`.
 
@@ -78,7 +78,7 @@ macro testapi(learner, data...)
     quote
         import LearnTestAPI.Test
         import LearnTestAPI.Serialization
-        import LearnTestAPI.MLUtils
+        import LearnTestAPI.MLCore
         import LearnTestAPI.LearnAPI
         import LearnTestAPI.InteractiveUtils
         import LearnTestAPI: @logged_testset, @nearly

src/tools.jl

@@ -62,8 +62,8 @@ const ERR_BAD_LENGTH = ErrorException(
 
 function get(learner_or_model, data, ::LearnAPI.RandomAccess, apply)
     observations = LearnAPI.obs(learner_or_model, data) |> apply
-    n = MLUtils.numobs(observations)
-    return MLUtils.getobs(observations, 1:n)
+    n = MLCore.numobs(observations)
+    return MLCore.getobs(observations, 1:n)
 end
 function get(learner_or_model, data, ::LearnAPI.FiniteIterable, apply)
     observations = LearnAPI.obs(learner_or_model, data)|> apply

test/learners/regression.jl

@@ -2,7 +2,7 @@ using Test
 using LearnAPI
 using LinearAlgebra
 using Tables
-import MLUtils
+import MLCore
 import DataFrames
 using LearnTestAPI
 
@@ -55,9 +55,9 @@ learner = LearnTestAPI.Ridge(lambda=0.5)
 
     fitobs = LearnAPI.obs(learner, data)
     predictobs = LearnAPI.obs(model, X)
-    model = fit(learner, MLUtils.getobs(fitobs, train); verbosity=0)
+    model = fit(learner, MLCore.getobs(fitobs, train); verbosity=0)
     @test LearnAPI.target(learner, fitobs) == y
-    @test predict(model, Point(), MLUtils.getobs(predictobs, test)) ≈ ŷ
+    @test predict(model, Point(), MLCore.getobs(predictobs, test)) ≈ ŷ
     @test predict(model, LearnAPI.features(learner, fitobs)) ≈ predict(model, X)
 
     @test LearnAPI.feature_importances(model) isa Vector{<:Pair{Symbol}}
@@ -73,7 +73,7 @@ learner = LearnTestAPI.Ridge(lambda=0.5)
     @test predict(
         recovered_model,
         Point(),
-        MLUtils.getobs(predictobs, test)
+        MLCore.getobs(predictobs, test)
     ) ≈ ŷ
 
 end
@@ -91,9 +91,9 @@ learner = LearnTestAPI.BabyRidge(lambda=0.5)
 
     fitobs = obs(learner, data)
     predictobs = LearnAPI.obs(model, X)
-    model = fit(learner, MLUtils.getobs(fitobs, train); verbosity=0)
-    @test predict(model, Point(), MLUtils.getobs(predictobs, test)) == ŷ ==
-        predict(model, MLUtils.getobs(predictobs, test))
+    model = fit(learner, MLCore.getobs(fitobs, train); verbosity=0)
+    @test predict(model, Point(), MLCore.getobs(predictobs, test)) == ŷ ==
+        predict(model, MLCore.getobs(predictobs, test))
     @test LearnAPI.target(learner, data) == y
     @test LearnAPI.predict(model, X) ≈
         LearnAPI.predict(model, LearnAPI.features(learner, data))